Master Thesis “deep Learning for Scalable

Vor 6 Tagen

Wien, Österreich Austrian Institute of Technology Vollzeit

As **Austria's largest research and technology organisation** for applied research, we are dedicated to make substantial contributions to solving the major challenges of our time, climate change and difitalisation. To achieve our goals, we rely on our specific research, development and technology competencies, which are the basis of our commitment to excellence in all areas. With our open culture of innovation and our motivated, international teams, we are working to position AIT as Austria's leading research institution at the highest international level and to make a positive contribution to the economy and society.

Our research focuses on developing **advanced deep learning frameworks** to predict **plant phenotypes**, such as “yield potential” and “disease severity”, in winter wheat using UAV-acquired multispectral data. Our aim is to build a scalable framework that leverages time-series multispectral images for efficient and **accurate phenotype prediction**

MASTER THESIS “DEEP LEARNING FOR SCALABLE PHENOTYPING USING UAV-ACQUIRED MULTISPECTRAL DATA”

CENTER FOR VISION, AUTOMATION & CONTROL
- Throughout this master’s thesis, you will closely work with our researchers and engineers, who focus on the development and deployment of assistive and autonomous systems, particularly unmanned aircrafts and land-based mobile machines - Hereby you will communicate and engage with a multi-disciplinary team to solve diverse multi-facetted challenges.
- Under the guidance of our experts, you will contribute to a rapidly growing field in aerial robotics.
- You design, develop, and optimise deep learning models for processing time-agnostic multispectral image data.
- You adapt and benchmark our model to improve the prediction of winter wheat phenotypes.
- You preprocess and manage large-scale UAV-acquired multispectral datasets.
- You collaborate with an interdisciplinary team of researchers in a supportive and dynamic environment.
- You conduct in-depth analysis and reporting of results, focusing on model interpretability and performance.

**Your qualifications as an Ingenious Partner**:

- Current enrollment in a Master’s programme in Computer Science, Geoinformatics, Remote Sensing, Mathematics, Statistics, Physics, Engineering, or a related STEM field.
- Strong foundation in machine learning, with experience in deep learning frameworks (e.g., PyTorch, TensorFlow).
- Familiarity with computer vision techniques, especially convolutional neural networks and attention mechanisms.
- Ability to work both independently and as part of a collaborative research team.
- Good knowledge of verbal and written English

**What to expect**:

- **Duration of the master’s thesis project**: 6 months
- **Start date**: ideally 01.12.2024 - However there is some flexibility concerning the start-date
- EUR 968 gross per month for 20 hours/week based on the collective agreement. There will be additional company benefits. As a research institution, we are familiar with the supervision and execution of master theses, and we are looking forward to supporting you accordingly

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